头部姿势和面:面部识别算法的自动化挑战?
Petra Urbanova1, Tomas Goldmann2, Dominik Cerny1
1Department of Anthropology, Faculty of Science, Masaryk University, Czech Republic.
概括
自动面部识别的准确性随着头部姿势的变化和某些表情而下降. 性能主要受到向上头部运动的影响,并且在两性之间存在差异,男性的不相似性得分更高.
科学领域:
- 生物识别和法医科学
- 计算机视觉和人工智能的人工智能
背景情况:
- 目前的生物识别和商业图像处理在很大程度上依赖于人工智能和机器学习的准确性.
- 这些人工智能系统的"黑盒子"性质引发了对法医应用程序透明度和问责制的担忧.
研究的目的:
- 调查面部表情和头部姿势对自动面部识别准确性的影响.
- 在不同的面部和姿势条件下分析最先进的算法 (ArcFace) 的性能.
主要方法:
- 利用了41名参与者的3D面部数据,生成了9个表情和头部姿势的2D图像,从-45°到45°不等.
- 使用ArcFace算法对超过5400万个不相似度得分进行了对比.
- 分析了摇头和摇头运动,不同表情和性别对识别准确性的影响.
主要成果:
- 轻微的头部偏差的影响最小,但由于目标偏离了正面视图,准确性显著下降.
- 向上俯冲和右向左俯冲的运动比向下俯冲和左向右俯冲更有害.
- 男人表现出比女性更高的不相似度得分,特别是在向上的头部运动中.
结论:
- 面部表情和头部姿势显著挑战自动化面部识别系统,特别是在极端的角度.
- 绩效中的性别差异突出了当前算法中的潜在偏差.
- 需要进一步的研究,以提高面部识别技术在法医和生物识别环境中的稳定性和公平性.
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